In a typical radio system, information is modulated onto a radio carrier by a transmitter. This signal then travels via an unknown and changing environment to the receiver. The ability to estimate the characteristics of this propagation environment and to mitigate the impact on the received signal is often key to the performance of a receiver.
FIG. 1 depicts various processing stages that form part of such an approach. It should be noted that the blocks shown in FIG. 1 represent processing operations performed on a received signal but do not necessarily correspond directly to physical units that may appear within a practical implementation of a receiver. The first stage 101 corresponds to the radio frequency processing. During the radio frequency processing, the received signal is down-converted to base-band using a mixer 103. The reference frequency used to drive the mixer is generated by an oscillator 104. Following this carrier down-conversion, the signal is low-pass filtered 102 and then passed to the mixed-signal processing stage 108. The mixed signal processing includes Analogue-to-Digital Conversion (ADC) 105, sampling 106 and low pass filtering 107. The resulting signal, which is now digital, is supplied to the digital signal processing stage 111 where it is processed such that the transmitted information can be recovered. The received signal is first processed by the channel estimation unit 109 where an estimate of the Channel Impulse Response (CIR) is generated. This estimated CIR is processed in combination with the received signal by the demodulation unit 110 such that the sequence of transmitted bits can be recovered.
In the downlink of cellular communication systems, a pilot signal is usually transmitted in combination with the information bearing signals such that the receiver can estimate the propagation channel. For Wideband Code-Division Multiple Access (W-CDMA) schemes, this pilot signal is typically code-multiplexed with the transmitted signal. For example, in the 3GPP standard, the Common Pilot Channel (CPICH) is a sequence of known bits which are modulated, spread and added to the downlink signal (3GPP TS 25.211; Technical Specification Group Radio Access Network; Physical channels and mapping of transport channels onto physical channels (FDD)). At the receiver, it is possible to generate an estimate of the CIR by correlating the received signal with the known CPICH pilot sequence.
The accuracy of the channel estimation process is crucial in determining the quality of the demodulation process. For W-CDMA systems, it is typical to use a Rake architecture at the receiver (CDMA—Principles of Spread Spectrum Communication, Andrew J Viterbi, Addison-Wesley Wireless Communications Series). In the Rake receiver, the weights associated with the different fingers correspond to the estimated CIR taps at the finger delay locations. The noise affecting these finger weights increases the likelihood of errors in the demodulation process. More recently, new receiver architectures have been introduced where the demodulation accuracy is improved at the expense of the implementation complexity. The Linear Minimum Mean Square Error (LMMSE) equaliser is an example of such an architecture (Chip-Level Channel Equalization in WCDMA Downlink, K Hooli, M. Juntti, M. J Heikkila, P. Komulainen, M. Latva-aho, J. Lilleberg, EURASIP Journal on Applied Signal Processing, August 2002). The LMMSE equaliser improves the performance of the demodulation unit by mitigating the distortions introduced by the propagation channel. The LMMSE equaliser can be implemented using a pre-filter Rake architecture (Equalization in WCDMA terminals, Kari Hooli, PhD thesis, 2003) where the conventional Rake receiver is preceded by a linear filter which aims at removing the Inter-Symbol Interference (ISI) introduced by the channel. In the pre-filter Rake receiver, the channel estimates are used both to set the weights of the Rake receiver as well as to derive the coefficients of the linear pre-filter. Hence, noise in the channel estimation process will significantly degrade the receiver performance.
High-Speed Downlink Packet Access (HSDPA) is an evolution of the Release 99 version of the 3GPP standard aimed at providing improved user experience through increased data rates and reduced end-to-end latency. These improvements are delivered through a combination of Incremental Redundancy (IR) and the use of higher-order modulation schemes. HSDPA extends the capabilities of 3GPP by introducing the use of the 16QAM modulation for the data bearing channels. Compared to the QPSK modulation scheme used in 3GPP, 16QAM is more sensitive to errors in the estimation of the amplitude of the channel. Hence, accurate channel estimation is important to the performance of an HSDPA receiver.
As indicated earlier, it is possible in a W-CDMA system for the receiver to estimate the CIR by correlating the received signal with the known pilot signal. It should however be stressed that the estimated CIR will be degraded by noise and interference. The signals transmitted to the different users in the system, both in the same cell as the user of interest and adjacent cells, will generate an increased level of noise in the channel estimates. The noise level in the channel estimates is determined by the properties of the cross-correlation between the pilot signal and the other user signals. Even in the absence of interfering signals from other users present in the system, the channel estimation process will suffer from self-interference generated by the non-perfect properties of the pilot signal auto-correlation.
The present application presents a number of techniques that can be used to improve the accuracy of the channel estimates.
The period at which channel estimates can be generated depends on the format of the pilot signal being used. For example, in the 3GPP standard, it is possible to generate a new set of channel estimates for every new 512 chips. It should however be noted that variations in the channel estimates generated at each update period will depend on the propagation environment, specifically the Doppler frequency of the channel. When the User Equipment (UE) is moving slowly in the system, successive channel estimates are heavily correlated. It is therefore possible to improve the channel estimates by filtering them across multiple update periods. Such an approach has already been proposed in ‘Adaptive Filtering for Fading Channel Estimation in WCDMA Downlink, Petri Komulainen and Ville Haikola’. In order to achieve good performance, it is important to match the characteristics of the filter applied to the channel estimates to that of the propagation environment. In ‘Adaptive Filtering for Fading Channel Estimation in WCDMA Downlink, Petri Komulainen and Ville Haikola’, this is achieved by using a filter where coefficients are adapted to the channel characteristics using the Least Mean Square (LMS) algorithm.